Deadbeat Predictive Current Control Research Articles

A New Sliding-Mode Observer-Based Deadbeat Predictive Current Control Method for Permanent Magnet Motor Drive

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy.

Improved Deadbeat Predictive Current Control With Embedded Resonant Polynomial and Disturbance Observer for PMSM Current Distortion Rejection

Improved Deadbeat Predictive Current Control With Embedded Resonant Polynomial and Disturbance Observer for PMSM Current Distortion Rejection

A Fast-Response PV Simulator Based on Improved Deadbeat Predictive Current Control

A Fast-Response PV Simulator Based on Improved Deadbeat Predictive Current Control

Model-free predictive current control based on improved sliding mode disturbance observer

The motor parameters of permanent magnet synchronous motor (PMSM) can be mismatched in operation due to temperature variations and magnetic saturation. The parameter mismatch leads to a significant decrease in the robustness of traditional deadbeat predictive current control (DPCC), which leads to issues such as current static error and motor vibration noise. In this paper, a model-free predictive current control (MFPCC) method based on an improved sliding mode disturbance observer (SMDO) was proposed. The method estimated the total disturbance of the system using the improved SMDO, which could effectively suppress sliding mode chattering and accelerate the convergence speed of current errors. Subsequently, the predicted control voltage was calculated using a hyper-local model and compensated for the time delay. The experimental results demonstrated that the improved MFPCC-SMDO method generated lower motor noise compared to the traditional DPCC method when the controller’s inductance parameters are mismatched.

RLS-based deadbeat predictive current control for dual three-phase segmented powered linear motors

RLS-based deadbeat predictive current control for dual three-phase segmented powered linear motors

Enhanced deadbeat predictive current control with novel generalized space vector modulation for five‐level inverter fed permanent magnet synchronous motor drive with reduced torque ripples and less computational burden

SummaryTraditional electric drive control methodologies, such as field oriented control (FOC) and direct torque and flux control (DTFC), predominantly rely on multiple proportional‐integral (PI) controllers that demand complex tuning. While model predictive current control (MPCC) omits PI controllers, it has inherent challenges including high computational burden and tempered dynamics. Although two‐level inverters (2LI) are prevalent in many applications, their performance wanes in high‐power applications like high performance industrial drives and marine propulsions. Multilevel inverters (MLIs) are more efficient, but combined with space vector modulation (SVM), they add computational complexity, particularly at higher levels, limiting drive sampling frequencies and causing torque ripples and harmonics, affecting performance. To address the above drawbacks, this manuscript presents a deadbeat predictive current control of five‐level inverter fed permanent magnet synchronous motor (PMSM) drive with a novel generalized SVM algorithm with reduced computational burden and improved performance. The proposed method is validated through MATLAB/Simulink‐based simulation study and experimental implementation. The results are presented and discussed. The results are compared with DTFC and MPCC where it is observed that the proposed method offers superior performance with reduced computational burden and streamlined implementation.

Dual Model Predictive Control Strategy of Direct‐Drive PMSM Based on Sliding Mode Disturbance Observer

Aiming at the current control steady‐state error caused by sampling delay, parameters mismatch of direct‐drive permanent magnet synchronous motor (PMSM). This paper proposed a method of dual model predictive control (MPC) of direct‐drive PMSM based on sliding mode disturbance observer (SMDO). Deadbeat predictive current control (DPCC) is used for current loop, a SMDO is proposed to observe the system disturbance, the estimation is compensated to DPCC by feedback to decrease the current control steady‐state error. For speed loop, MPC with an internal model control observer (IMCO) can observe and compensate the load disturbance feedforward. The experimental results show that, the dynamic response speed of the proposed strategy is fast, the speed recovery time after sudden load torque is short, and the anti‐load disturbance performance and current tracking performance are effectively improved. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Simple Deadbeat Predictive Current Control for OW-PMSM Drives Based on Reference Voltage Redistribution

A Simple Deadbeat Predictive Current Control for OW-PMSM Drives Based on Reference Voltage Redistribution

An Improved Deadbeat Predictive Current Control Based on Parameter Identification for PMSM

An Improved Deadbeat Predictive Current Control Based on Parameter Identification for PMSM

Improved Deadbeat Predictive Current Control with Extended State Observer for Dual Three-Phase PMSMs

Improved Deadbeat Predictive Current Control with Extended State Observer for Dual Three-Phase PMSMs

Improved Deadbeat Predictive Current Control of PMSM Based on a Resistance Adaptive Position Observer

Improved Deadbeat Predictive Current Control of PMSM Based on a Resistance Adaptive Position Observer

Model-Free Deadbeat Predictive Current Control for SPMSM Based on Adaptive Gain Extended State Observer

Model-Free Deadbeat Predictive Current Control for SPMSM Based on Adaptive Gain Extended State Observer

Switching Active Disturbance Rejection Based Deadbeat Predictive Current Control for Permanent Magnet Synchronous Motors

Switching Active Disturbance Rejection Based Deadbeat Predictive Current Control for Permanent Magnet Synchronous Motors

Enhanced Deadbeat Predictive Current Control for PMSM Drives Using Iterative Sliding Mode Observer

Enhanced Deadbeat Predictive Current Control for PMSM Drives Using Iterative Sliding Mode Observer

Deadbeat Predictive Current Control for Surface-Mounted Permanent-Magnet Synchronous Motor Based on Weakened Integral Sliding Mode Compensation

Deadbeat predictive current control (DPCC) has excellent dynamics and can achieve current control with less computational effort. However, its control performance relies on the precision of the parameters of the motor. Current static error will be generated and control performance will be decreased when the predictive model parameters do not correspond to the practical parameters of the motor. In this article, a weakened integral sliding mode compensation method is proposed which converts the current error into a voltage compensation term and adds it to the prediction control output to effectively compensate for the steady-state error. In addition, a boundary layer is introduced to weaken the integral so as to solve the problems of integral saturation and overshoot caused by the introduction of the integral term when the error is large. Moreover, weight factors are introduced to optimize the feedback current, which enhances the robustness of the system. In the end, the effectiveness of this method is verified via simulation and experimental results.

Output current harmonic analysis and suppression method for PMSM drive system with modular multilevel converter

AbstractAiming at the output current distortion problem in the low frequency operation of permanent magnet synchronous motor (PMSM) drive system of modular multilevel converter (MMC), the essential relationship between the output current distortion and the injected components is analyzed in this paper. A current harmonic suppression method combining feedforward and feedback compensation is proposed. Firstly, the feedback control method is applied to transform the MMC three‐phase output current into the dq rotating coordinate system at fundamental frequency ω. The harmonics of the AC terms to be suppressed are filtered out. The dominant fundamental frequency component turned into the DC term is retained. Then combined with feedforward control, the control performance of the system is improved. Secondly, the deadbeat predictive current control is improved by introducing an integral link in it. System disturbances caused by parameter changes can be compensated in real time. The design of the control system is simplified and the static error is reduced. Meanwhile, the system has the advantages of small torque pulsation and fast speed response. Finally, the PMSM drive system model of the 11‐level MMC is built in MATLAB/Simulink. The correctness and effectiveness of the proposed suppression method are verified.

An Improved Model-Free Active Disturbance Rejection Deadbeat Predictive Current Control Method of PMSM Based on Data-Driven

This article proposes an improved model-free active disturbance rejection deadbeat predictive current control(ADRDPCC) method for permanent magnet synchronous motor (PMSM) used in more electric aircraft (MEA) based on the data driven method, which is used to solve the parameter mismatch problem of deadbeat predictive current control (DPCC) and improve the performance of PMSM control system. DPCC model applied to the current loop of the MEA motor is established as the main control strategy of the system. The principle of active disturbance rejection control is combined with DPCC, and the ADRDPCC structure is formed to optimize the control strategy. A specific extended state observer (ESO) of ADRDPCC is designed to track the internal disturbance caused by parameter mismatch and the external disturbance in real time. DPCC is used as the control law of the ADRDPCC structure to predict the current and output the reference voltage based on the observation of ESO. A deep reinforcement learning model based on ADRDPCC is designed and trained based on the data-driven method. The trained model can compensate and optimize ADRDPCC based on the disturbance observed by ESO and the observed control state of PMSM. The simulated and experimental results show the superiority of the proposed method.

A Robust DPCC for IPMSM Based on a Full Parameter Identification Method

This paper proposes a robust deadbeat predictive current control (DPCC) method for the interior permanent magnet synchronous motor (IPMSM) based on a full parameter identification method. The DPCC method is innovatively combined with the high frequency (HF) currents injection method, which can observe inductances, flux linkage, and resistance values of the IPMSM online. After the parameter identification results are obtained, the initial crude parameters are replaced by the identification values in the DPCC method to enhance the robustness of the IPMSM control system. Moreover, a novel online accuracy verification method for parameter identification results is proposed to judge the error estimation level. The proposed method extracts the high bandwidth and the high parameter sensitivity characteristic of the DPCC method. The online identification and verification of IPMSM parameters are proceeded subsequently while achieving robust control of the IPMSM with the DPCC method. Finally, the proposed method is experimentally demonstrated with a 1.5kW motor. The experimental results show that the IPMSM with the proposed method can still work well even in the situation of severe initial parameters mismatch.

Fault-tolerant control strategy of six-phase permanent magnet synchronous motor based on deadbeat current prediction.

The fault-tolerant control after phase loss is crucial in the studies of the six-phase permanent magnet synchronous motor (PMSM), and the one phase loss is the most frequent phase loss fault. To improve the system instability caused by nonlinear and time-varying perturbations of inductance parameters in a double-Y phase shifted 30° six-phase PMSM, an improved deadbeat predictive current fault-tolerant control (DPC-FTC) method is proposed in this study. The transformation matrix after single-phase open-phase is first reduced and reconstructed, and the reduced-dimensional voltage equation is derived. Based on this equation, the deadbeat current predictive control is then used to predict the expected voltage using the current feedback value and the reference value, so as to shorten the response time and improve the overall control effect. The voltage equation after parameter perturbation is rewritten, and the current discrete transfer function under constant expected voltage before and after parameter perturbation is calculated. Afterwards, to further improve the low stability of fault-tolerant control after phase loss, which is caused by the inductance parameter perturbation of the control system, the weight coefficient is introduced in order to enhance the deadbeat predictive current control so that it splits and optimizes the direct-quadrature axis current. The stability of the system is then analyzed. By changing the weight coefficient, the fault-tolerant control system has a wider stable working range. Finally, the simulation model and experimental platform are completed. The results show that the improved DPC-FTC method improves the permissible inductor parameter uptake range by a factor of 1/β, reduces the current static difference by 32.05% and 46.02% when the inductor parameter is mismatched by a factor of 2, reduces the current oscillation and effectively reduces the sensitivity of system stability to inductor parameter uptake.

Parameter Robust Deadbeat Predictive Current Control for Open-Winding Surface Permanent Magnet Synchronous Motor Drives

Deadbeat predictive current control (DPCC) of open-winding surface permanent magnet synchronous motor (OW-SPMSM) has excellent dynamic and steady-state performance, but it depends heavily on the accuracy of motor model parameters (resistance, inductance, and flux linkage). To eliminate the influence of parameter mismatch and retain the fast dynamic performance of the traditional DPCC, a parameter robust DPCC for OW-SPMSM drives is proposed in this paper. Firstly, the incremental prediction model of OW-SPMSM is introduced which eliminates the flux linkage parameters in traditional DPCC. Secondly, the parameter sensitivity analysis shows that the mismatch of resistance parameter hardly affects the current prediction errors of incremental prediction model, which are mainly caused by the mismatch of inductance parameters. Finally, to extract the actual inductance information, an inductance extraction strategy is proposed. Experimental results show that the current control performance of the proposed robust DPCC is not influenced by any motor parameter mismatch, which always has smaller current tracking errors and total harmonic distortion (THD) than traditional DPCC. Moreover, compared with the existing robust DPCC with observers, the proposed DPCC has better dynamic response performance.